Deep Learning - Deep Neural Network for Beginners Using Python - Maximum Likelihood Part 2

The video explores the inverse relationship between error and probability in model evaluation. It highlights the flaws of using product methods for calculating accuracy, as a single zero can nullify results. Instead, it suggests using sums, with logarithms as a mathematical function to convert products into sums, maintaining the method's essence.

What is the relationship between error and probability in a model?

• Directly proportional

• Inversely proportional

• Unrelated

• Equal

Why is using products to evaluate model accuracy problematic?

• Products are not mathematically valid

• Products are too complex to calculate

• Products always give inaccurate results

• A single zero value can nullify the result

What is a potential issue when using products in model evaluation?

• They are too simple

• They can be easily manipulated

• One high value can disproportionately affect the outcome

• They require too much data

Which mathematical function can convert products into sums?

• Sine

• Cosine

• Logarithm

• Exponential

What is the advantage of using sums over products in model evaluation?

• Sums prevent a single value from dominating the result

• Sums are universally accepted

• Sums are more accurate

• Sums are easier to calculate